As early as 2010, The Economist reported a new trend in connectivity – cows. While rarely considered cutting-edge technology, cows were being wired by a Dutch startup to let farmers know when they are sick or needed milking. This allowed the cows natural free-range movement since farmers had to no longer worry about missing distress signals.

If lumbering bovines can be wired and connected, anything can. But for a number of reasons, manufacturing and industrial sectors have fallen behind in the Internet of Things (IoT) revolution. The World Economic Forum reports “the vast majority” of industrial organizations that are struggling to adapt to IoT technology and face a serious risk of being left behind. The Industrial IoT (IIoT) needs a technology boost. By embracing secure and reliable over-the-air (OTA) software and firmware upgrades, manufacturing industries can fully harness the power of IoT and maintain a competitive edge.

The Challenge of IIoT

In a groundbreaking whitepaper, analysts at Cisco described the era of IoT as “the point in time when more ‘things or objects’ [are] connected to the Internet than people.” We are at that time. Gartner estimates there will be 8.4 billion connected devices in 2017, a 31% leap from 2016. It is an industry worth hundreds of billions of dollars, and it is growing.

So why does the manufacturing sector still lack an IIoT infrastructure? Here are some of the reasons:

Lack of Security: This might be the most crucial factor. IoT communication is vulnerable without the right security platform. Much like the automotive industry (though unlike smartphones), manufacturing industries view lack of security as more than just a financial risk, it can pose a physical danger. For instance, if a company’s communication is compromised and its assembly line components receive conflicting orders, the resulting chaos could damage machines and pose a threat to the humans operating them.

Far-Flung Infrastructure: Imagine a deep-sea mining operation with headquarters in Australia, offices in Miami, Singapore and Amsterdam, and equipment all over the world. If a vastly complex operation relies on connected technology, it is a massive task to ensure that their equipment remains synced. If the system needs updates, technicians will have to fix hundreds of separate units in remote locations. Maintaining this kind of communication is expensive. In fact, a recent study shows that communication is currently responsible for 35-50% of total IIoT costs.

Lifecycle Management: When a smartphone is developed, manufacturers expect it to be in the field for only about two years. This is not the case with industrial manufacturing. A field of wind turbines will not be replaced by next year’s model – they will operate for at least 20 or 30 years. This longevity creates enormous challenges. IoT technology advances rapidly, and updating equipment over such a long time span is prohibitively expensive and creates a nightmare for logistics.

How Does an OTA Infrastructure Enable the IIoT?

What all these problems have in common is their ability to be solved with dedicated and secure OTA communication. Right now, the auto industry is at the forefront of smart OTA updates as they work to enable self-driving cars. They are collaborating with tech companies and regulatory bodies to develop the necessary infrastructure to make V2V (Vehicle to Vehicle), V2I (Vehicle to Infrastructure), and V2X (Vehicle to Everything) communication possible.

A comprehensive OTA platform for IIoT enables the same innovations. Setting up a secure and reliable network lets companies receive data, communicate information, and send SOTA and FOTA upgrades to equipment all over the globe. A company with plants in Duluth, Mexico City, Johannesburg, and Bangalore will be able to keep all machines in constant communication, no matter how many software upgrades they go through and how long the lifespan of each piece of equipment is.

The core of the global economy is the manufacturing industry. All over the world, factory engineers, manufacturers, supply chain experts, ship captains and workers are united by the motivation to create something new. This is the backbone of the world and it needs to take advantage of technology but can only do so with a strong and secure OTA infrastructure. We are on the brink of world-changing technology that is set to be worth trillions of dollars across the manufacturing sector. Manufacturing businesses need to understand that doing things the old, unconnected, pre-IoT way is not going to be beneficial in the long run.

As the auto industry is changed by technological and economic currents, OEMs and Tier-1 manufacturers will need to partner with technological specialists to thrive in the era of the software defined car. Movimento’s expertise is rooted in our background as an automotive company. This has allowed us to create the technological platform that underpins the future of the software driven and self-driven car. Connect with us today to learn more about how we can work together.

]]>Autonomous Taxi Fleets in Singapore Help OEMs Navigate the Global Markethttps://movimentogroup.com/blog/autonomous-taxi-fleets-singapore-help-oems-navigate-global-market/
Tue, 05 Dec 2017 17:36:44 +0000https://movimentogroup.com/?p=5338One and a half year prior to the acquisition by Aptiv, in the summer of 2016, a month before Uber ...

One and a half year prior to the acquisition by Aptiv, in the summer of 2016, a month before Uber started experimenting with fleets of self-driving taxis in Pittsburgh, another event took place – NuTonomy, a company based out of Cambridge, debuted their autonomous taxi fleet in Singapore, becoming the first company in the world to test self-driving cars with customers.

This test was remarkable for many reasons. The technology was groundbreaking, the concept of a car as a robot was paradigm-turning, and NuTonomy’s cooperation with the Singaporean government provided a collaborative model for other countries, including the United States. While Uber, with its enormous market share, might have received a very deserved amount of attention, NuTonomy is setting an example for OEMs and technology companies that are looking to navigate global markets.

NuTonomy’s Singapore Tests

NuTonomy unveiled its fleet of self-driving taxis in August 2016. The company does not operate in the same way as Uber does. NuTonomy opted to test on a much smaller, more controlled scale.

In initial tests, the company modified six cars (Renaults and Mitsubishis) to enable them to be self-driven. While Uber had their driverless cars pick up passengers randomly, with no advance notice, the Singapore test required riders to sign up in advance. In addition, NuTonomy’s vehicles could only use pre-determined pickup points within a 2.5 square mile radius (although to be fair, the city-state of Singapore is only about 227 square miles total).

Like Uber, rides were free and featured an engineer in the driver’s seat, both to maintain emotional continuity and to step in if anything went wrong. Nothing really did, although the staff of ReCode rode in one of the self-driving cars in April and noticed a few minor issues, such as the vehicle’s tendency to stop suddenly and maintain an unusual amount of space from the cars on the side of the road. Still, they were impressed, writing:

“Clumsy as it may have been, the car — which is still very much in the research and development phase — was navigating a more complex environment than other autonomous car companies typically test in. Many carmakers, in fact, have yet to even begin testing autonomous vehicles around pedestrians.”

Being able to understand traffic lights, navigate to a destination and not just detect obstacles but figure out when and how to pass them is no small feat for an autonomous vehicle. Often, that clumsiness was simply a result of the vehicle being overly careful and leaving considerable space between it and the object it was skirting.”

These successes have led NuTonomy to take on an optimistic turn. Thus, they started testing the technology in their hometown of Boston. They have been cruising through the city’s notoriously terrible traffic since January 2017 and more than the hills and bridges of Pittsburgh, the cranky drivers of Beantown promised to be an important test.

However, Boston is not the company’s primary goal. The founders of NuTonomy are already thinking beyond test drives, hoping to have fully self-driving taxis on the market in Singapore as early as 2019, years before their competitors. The reason for NuTonomy’s rapid developmental pace is not just their technology, but Singapore itself.

Understanding the Impact of Local Governments

“The main reason is that the Singaporean government has realized that self-driving vehicles could have a significant positive impact on the economy, the transportation efficiency, and on the public health and safety of transport in Singapore. The regulatory environment is very capable, the infrastructure is very good, the weather is favorable for testing and developing the technology, and there are good driving practices and adherence to driving rules in Singapore.”

These considerations are all extremely important. For any new technology to take off, the conditions have to be exactly right. This goes above and beyond technical capabilities. Look at, say, the computer industry in Southern California in the 50s and 60s, it might not have flourished, was it not for a working patent system, investment in aerospace technology, and the massive diversions of the Colorado River that allowed Los Angeles and the surrounding area to grow. Such favorable circumstances frequently enable the development of technology.

That is exactly what Singapore offers. After years of the “benevolent authoritarianism” of Lee Hew Kuan, Singapore is a highly-developed state with an incredible and easy-to-use infrastructure. It is an ideal spot to launch technology that depends on clear, consistent communication. If roads were shaped at odd angles, with lots of potholes, too many blind spots, and pedestrians who jaywalked, the tests might have fizzled out before they even began.

As NuTonomy demonstrates, being able to understand local conditions and strategically adapt to them will be key to unlocking foreign markets. Autonomous cars will require enormous amounts of cooperation between OEMs, tech companies, and of course governments in order to really take off, and not all locations will be as amenable as Singapore.

OEMs and their partners might have to invest more in infrastructure in some locations or work with the government to help foster the right kind of educational and regulatory environments that enable countries to be equal partners in technological progress. All this can start by proving success in ready markets like Singapore, Taiwan, or Malaysia. Developments there can pave the way for the rest of the globe. Of course, we must also have the right technology.

The Car as a Robot and the Importance of New Ways of Thinking

One thing that differentiates NuTonomy from other self-driving systems is that it uses a “hierarchy of rules,” allowing the cars to break rules based on given inputs and adaptive learning. As NuTonomy COO, Dour Parker, told ReCode, “NuTonomy cars use formal logic. Essentially, we establish a hierarchy of rules and break the least important. For example, one rule is ‘maintain speed.’ Another is ‘stay in lane.’ We violate the ‘stay in lane’ rule because maintaining speed is more important.”

This logic seems to be derived from the way NuTonomy conceptualizes their cars. The company is an MIT-spinoff, run essentially by robotics geniuses. They did not think of their technology as ‘cars becoming more automated’, they thought of it as ‘robots learning to drive’. Even if it is, in the end, the same thing, it is an important conceptual difference, and NuTonomy’s success suggests that such creative thinking is a model to be followed.

In short, new cars require new ways of thinking. It is this shift in perspective that allows us to find fresh approaches to existing problems. Viewing the connected car as a human body, for instance, allows us to understand the car as a holistic infrastructure rather than a collection of parts. Cars are no longer just a way to get from home to another destination; with connected services, they are now an evolving ecosystem that can be continuously updated over-the-air.

It is a different world. As OEMs continue to evolve their ways of thinking and learn to operate in increasingly complex international markets, they must seek out partnerships with progressive technology companies that help bring different perspectives to the table. Partnerships extend both ways, of course, and young companies also have a lot to learn from OEMs’ decades of institutional expertise.

Thanks to the globe-changing efforts of OEMs and technology companies, we already have a glimpse of the future – someday soon, self-driving cars will give people rides not just within a 2.5-mile radius or through the streets of Boston, but everywhere around the world.

As the auto industry is changed by technological and economic currents, OEMs and Tier-1 manufacturers will need to partner with technological specialists to thrive in the era of the software defined car. Movimento’s expertise is rooted in our background as an automotive company. This has allowed us to create the technological platform that underpins the future of the software driven and self-driven car. Connect with us today to learn more about how we can work together.

]]>Can Big Data Program Morality in Self-Driving Cars?https://movimentogroup.com/blog/can-big-data-program-morality-self-driving-cars/
Tue, 28 Nov 2017 17:15:32 +0000https://movimentogroup.com/?p=5327You are driving down the street at 35 miles an hour. Suddenly, a dog runs onto the road. Without thinking ...

You are driving down the street at 35 miles an hour. Suddenly, a dog runs onto the road. Without thinking — because it happens too fast to think — you swerve into oncoming traffic to avoid it, crash into the parked car to your right, or slam on your brakes, knowing that there is almost no chance of avoiding a collision. What do you do?

Now imagine the dog is a child and that you have your own child in the car, and then the same thing happens. Scary, right? How do we make these sudden decisions that happen and then last forever? Who are we when we have no time to consider what to do?

This is not just raw philosophy. It is a question that engineers, programmers and OEMs must think about as they create responsive algorithms for self-driving cars. A self-driving vehicle is, in some ways, a moral agent with a programmed but lesser immediate set of ethics and responsibilities.

How can something so nuanced be programmed? How can a choice like this be made without direct human interaction? The answer potentially (and seemingly paradoxically) lies in the realm of big data – raw numbers that can somehow be transformed into a sense of morality. This is why OEMs need to take big data seriously by creating datasets that allow an actual sense of ethics to develop. To do so, we must also look at how our own ethics and personalities form.

Memory as the Font of Personality

Once, a friend recalled to me one of his first real memories – he was about four and was at his sister’s third birthday party. He wanted her balloon and knocked it out of her hand. Of course, it immediately went soaring up into the sky, to the sound of wailing tears below. The moment stuck with him his entire life, and he has tried (with some success) to never again covet a balloon.

More than that, the childish sadness of the day played a role in his personality. He credits it with making him more cautious and more concerned about people’s feelings. It was not just that one incident, of course; there are thousands of these moments throughout our lives with decisions and revisions that define who we are.

Some psychologists argue that memory is one of the key facets of someone’s personality. At a TED talk, one of the founders of behavioral economics, Nobel Prize-winner Daniel Kahneman, argued that when we think of how to behave “we actually do not choose between experiences, but rather choose between memories of experiences,” meaning that our memory of an experience is more responsible for how we act rather than what actually happened. In other words, memory drives our actions, shaping the reality in which we act.

We know how important this is, instinctively. When a person suffers from Alzheimer’s, we do not just talk about their memory fading in terms of the ability to conjure names and dates, but we also talk about the patient losing a sense of who they are. While some joyful research shows it is still there somewhere, we have accepted that when memory leaves us, we leave ourselves.

Thus, memory plays a crucial role in decision-making when we are on the road and face an unprecedented choice. While most drivers have never been in such a situation, they have been in moral situations before, which have affected who they are and what choices they make, allowing them to instinctively draw from a well of past experiences to make a decision. But how can a car do the same thing? From what well does it draw its decision-making skills?

Memory and Big Data

This is where big data comes in. Big data, in its essence, is a collected memory, a record of experience from all connected devices. Unlike human memory, the experience is not clouded by shame or pride or the usual fog of time. It is captured perfectly.

Big data collection actually mimics memory in some very important ways. As Emily Trinh at Bryn Mawr explains it, “Memory is also defined as the ability to retain information, and it is influenced by three important stages. The first stage is encoding and processing the information, the second stage is the storing of the memory, and the third stage is memory retrieval.”

Let us break that down and apply it to data:

Encoding and processing information: Automakers are already receiving a flood of information from connected cars, and that flood will continue to grow. In a few years, it is estimated that each connected car will produce 2 petabytes of data annually, capturing every rotation of the wheel and every interaction with other cars and the external environment. That data needs to be understood and processed accurately. One of the most innovative ways to do so is with graph databases, which mimic the mind by making quick connections between similar pieces of information.

Storing of the memory: Vast clouds of data need to be stored in a way that can allow the core algorithms powering connected cars to access them immediately. This means that the data cannot just to be dumped away — it must remain easily accessible.

Memory retrieval: This is key. Memory retrieval is not about sitting on the porch and reminiscing. It is about the car calling on a vast database to understand what to do in any given situation. If it is about to hit a pothole when it is raining and there are other vehicles directly adjacent to either side of the car, it has to call on the “memory” of every car who has ever been in such a situation and use this to deduce the right course of action. More than that, it must be able to find similar situations if there is no exact match and decide from there. All of this must happen instantly. It is real-time data.

This is where big data can become a source of morality, an essential set of ethics. The classic scenario dreamed by every ethicist is “What happens if a car has the choice of running into a group of children or killing the driver by swerving into a tree? Or if it must make a choice between hitting one person or two?” It is an example of the “trolley dilemma”. The implication, of course, is that a car does not have the same moral dependency and would fall back on a set code.

What if a broader memory based on big data can create a different set of ethics? What if that vast storage of scenarios means it has already experienced knocking a balloon out of a little girl’s hand, and not only does it never happen again but it can also help anyone else in a similar situation avoid the same mistake? With the collected data of experience, minus the adrenaline and fear, a car might be able to make a moral decision and avoid hitting anything or hurting anyone.

That is the dream and it is potentially achievable. Memories of the car and every other connected car form a vast web of information that can be drawn from, allowing each vehicle to always make the best decision. This is big data, shrunk suddenly down to the most human level.

As the auto industry is changed by technological and economic currents, OEMs and Tier-1 manufacturers will need to partner with technological specialists to thrive in the era of the software defined car. Movimento’s expertise is rooted in our background as an automotive company. This has allowed us to create the technological platform that underpins the future of the software driven and self-driven car. Connect with us today to learn more about how we can work together.

]]>Diversifying the Automotive Industry with the Five Levels of Autonomous Vehicleshttps://movimentogroup.com/blog/diversifying-automotive-industry-five-levels-autonomous-vehicles/
Tue, 21 Nov 2017 18:12:59 +0000https://movimentogroup.com/?p=5322The future of the automotive industry can be compared to rubber bands that come in an abundance of sizes and ...

The future of the automotive industry can be compared to rubber bands that come in an abundance of sizes and strengths. Even something as simple as a little ring of rubber has an enormous range of diversity. Something as complicated as the automotive industry has considerably more and the future of driving (and self-driving) will reveal an increasingly diverse and niche-driven industry.

This was demonstrated by Ford’s announcement in 2016 to put a self-driving fleet of cars on the road by 2021. The company wants to offer a car service, much like Uber (and recalling GM’s partnership with Lyft), where passengers can get a car to pick them up. They are already planning cars that will not have a steering wheel – cars that will not need any human involvement whatsoever. Instead of gradually moving towards fully autonomous cars, they seem to be willing to wait until they can offer a car with Level-4 autonomous features. Other automotive companies may soon face a similar choice – what level and type of autonomous features will they offer their customers?

The Five Levels of Automated Driving and What They Mean

As determined by the Society of Automotive Engineers(SAE), “The 5 Levels of Automated Driving” is a classification system that not only maps out the expected technological progress of self-driving cars but shows how autonomous vehicles can diversify the industry. The system categorizes different types of autonomous features, allowing automotive and technology companies (a distinction that is increasingly blurred) to choose between multiple business models.

There are four categories that the SAE looks at to determine a vehicle’s level of autonomy:

Execution of Steering and Acceleration/Deceleration

Monitoring of Driving Environment (basically, whose job it is to pay attention to the road)

Fallback Performance of Dynamic Driving Tasks (responding to events)

Driving Modes (total capabilities)

These levels only account for technical capabilities and do not imply a “better/worse” scenario, either in terms of progress or how each option will fare on the market. It is up to the OEMs to figure out which of the options customers will be most interested in as outlined below for the five levels of autonomy.

Level One: At this level, the car has some autonomous execution capabilities. The auto industry is already at level one and using early technology to make drivers more comfortable with the idea of autonomous vehicles, the last few years have seen the introduction of automated parallel parking and automatic braking. The actual driving, however, is still done by humans. Many automotive companies are focused on this level of technology, continuing to improve it in new models (and in older models via over-the-air software updates), with the end goal of helping customers realize that their cars are not static and that autonomous technology is safe.

Level Two: Level two is a significant step forward. Here, the vast majority of execution maneuvers are done by the driving system, which will handle the acceleration, braking and steering functions of the car. However, the driver must maintain control and continuously monitor the environment. Tesla has already started introducing level two cars which are significantly safer than human drivers but not yet foolproof. Level two vehicles are geared toward early adopters and have a strong foothold in the market, there will always be eager people to try out the latest technology. Level two cars will also be ideal for people who want to maintain some control as we transition to full automation, making this model unlikely to go away anytime soon.

Level Three: This is the great leap forward, where cars become qualitatively different. Here, the system monitors driving conditions and alerts the human driver only if intervention is needed. Level three allows for more relaxation and less alertness, the market for these vehicles seems poised to pick up after level two cars have paved the way. Level three vehicles are likely to be more expensive as they will have to be smart enough to navigate without much driver interference at a point in time when most of the other cars will only be semi-autonomous at best. These will be luxury vehicles and will have to be designed not just for functionality but also for beauty and aesthetic symbolism.

Level Four: At this level, cars are fully autonomous. The system will handle all fallbacks and will not require any driver intervention. Indeed, intervention might not even be possible (remember that Ford is reportedly designing their cars without steering wheels). At this point, the car is basically an extension of the home or office. The only catch is that these vehicles, only designed for specific environments, will not be able to just drive anywhere and will be mostly limited to urban or suburban driving. This is where Ford and its competitors enter the scene, their fleets of self-driving, on-demand cars are intended for urban use and will change the ownership model entirely, making level four cars particularly attractive for people who want to opt out of buying a vehicle.

Level Five: Fully autonomous and ready to go anywhere and “anywhere” refers to the highways and byways of the world that would be no longer out of reach. This is the last level of autonomous vehicles, for example, a car that can be used on a 500-mile road trip. Eventually, you might be able to call a car to take you to Yosemite, but long-distance driving is likely to be the domain of level three vehicles (which allow fallback control) and level five vehicles for quite some time. With such a range of options, automotive companies need to embrace diversity in their offerings. There will be drivers looking for level five cars (where not even the lack of a driver license should be an obstacle to “drive”) while some would still want level three cars (and others who will stick with level one for as long as they can).

The growth of the autonomous vehicle is like an evolutionary tree with many overlapping and intertwining branches. Just as Heidelbergensis, Neandertals, and Homo Sapiens may have passed each other on some distant plain, the various levels of self-driving cars will share the road in the near future. Automotive companies can diversify to include all, most, or just a couple of autonomous features, catering to many kinds of drivers and systems. They can also constantly update their software as the industry continues to evolve and grow.

As the auto industry is changed by technological and economic currents, OEMs and Tier-1 manufacturers will need to partner with technological specialists to thrive in the era of the software defined car. Movimento’s expertise is rooted in our background as an automotive company. This has allowed us to create the technological platform that underpins the future of the software driven and self-driven car. Connect with us today to learn more about how we can work together.

Uber took up an interesting “beer run” in late 2016. Partnering with Ottomotto, Uber completed a 120-mile freight delivery using a self-driving 18-wheeler truck. The package? 45,000 cans of Budweiser, moving from a weigh station in Fort Collins, Colorado to its final destination in Colorado Springs.

The technology for self-driving trucks has been steadily improving and this was the first major commercial demonstration of its ability. It represents not just new technology, but a new economic model, showing both how the shipping industry is changing and how OEMs must adapt to keep up.

The Importance of the Trucking Industry

In a country like the US that is so vast and yet so intimately connected to the global market, countless industries depend on the ability to move goods from one place to another. It is estimated that, as of 2012, trucks alone are responsible for shipping the following in a year:

8 billion tons of freight.

$13 trillion worth of goods.

70% of all US freight.

To put it in another perspective, roughly 1 out of every 7 people in the workforce is employed in the transportation system, in some way or the other. There are almost 3.5 million truck drivers in the US and another 5.2 million non-drivers who work in the industry. It is a booming business, and any change will have far-reaching repercussions.

Uber’s Demonstration: An Upcoming Economic Model

With its shipment of Budweiser, Uber has demonstrated that it is ready to change the rules of shipping. This is not the first time that we have seen trucks that are able to drive themselves – they have been at the forefront of autonomous technology longer than cars. In early 2016, a fleet of over a dozen autonomous trucks left from points in Germany, Belgium, Sweden, and Denmark and converged in the Netherlands. But there is a big difference between testing the technology and having actual proof of its commercial viability and Uber’s plan worked perfectly.

The most notable aspect of this plan is arguably that it was not just Uber’s. The technology belonged to a startup called Ottomotto, which had been working on fleets of self-driving trucks since January 2016. This marks a unique sort of partnership – more than just acquiring Ottomotto, Uber invested in them, putting $680 million into their technology over the summer of 2016 even though there is still a firewall between the companies in terms of process.

This type of partnership is a growing trend. Apple, for example, is no longer trying to produce a car and is instead focusing on software, suggesting that they may soon be looking for automotive partnerships. Ottomotto itself was founded by ex-Google employees who had been working on the company’s self-driving car project before venturing off on their own. The industry is flooded with startups — people who know technology or people who know automotive — pairing with companies that are experts in their field promises to be profitable for OEMs by enabling them to adapt to a new economy.

The Future of the Shipping Industry

These partnerships are especially relevant when it comes to the shipping industry. The Bureau of Labor Statistics estimates that we will actually need 21% more drivers in 2020, compared to 2010, and a growing industry means more partnerships and more scope for innovation. There will be a need for smart forklifts, pallet loaders, and other autonomous machines. Automotive knowledge and technology expertise will combine in fascinating ways to benefit both companies and their employees.

There is a lot of talk about self-driving trucks decimating jobs for many people, but OEMs can also partner with trucking companies to create flexible, streamlined shipping models that would use partial autonomous features. This would ease some of the burden on drivers and possibly create new jobs in maintenance, remote management, fleet management and operations in the process. We will still need human drivers for a long time. Even the Ottomotto model needed human guidance to steer off the highway at the Colorado Springs exit. There is still a long way to go before fully autonomous trucks become a reality and there are a lot of regulatory hurdles left to jump over.

During the transition, OEMs can partner with established trucking companies to create the next generation of vehicles – ones that will incorporate self-driving and autopilot functions but would still need human drivers. This will be a massive technological undertaking, of course, and will require Over-The-Air (OTA) software updating capabilities so that vehicles can be continuously improved as new technological advancements are developed. By partnering with companies that excel in OTA technology, OEMs will be able to embrace the future by keeping themselves at the forefront of this shifting industry.

As the auto industry is changed by technological and economic currents, OEMs and Tier-1 manufacturers will need to partner with technological specialists to thrive in the era of the software defined car. Movimento’s expertise is rooted in our background as an automotive company. This has allowed us to create the technological platform that underpins the future of the software driven and self-driven car. Connect with us today to learn more about how we can work together.

Every time a modern plane takes off, it represents a wonder of technological capability. Beyond the physics required to lift a 20-ton jet laden with passengers into the air, the communication technology necessary for each aircraft to remain safe and on-course is incredibly advanced.

Planes today communicate with each other, but they also communicate with the past – every commercial flight is essentially learning from the data of all other flights in modern history. Airlines use a shared database to create industry-wide knowledge of best practices, taking the wisdom of previous years and continuously applying it. This airline model of shared data is a potential solution to the problems that self-driving cars currently face but such a system will require paradigm shifts in how we define the information and would demand more responsive cybersecurity, but it will also allow the self-driving car to pass regulations and inspire confidence in customers easily.

Airlines, Cars and the Wisdom of the Ages

For years, all kinds of airlines have shared flight information with a third party, who pools and analyzes the data for safety information. Every jolt of turbulence, every anomaly, every close call and especially every accident is analyzed to find patterns and make flying safer.

This works as the number of annual flights keeps increasing, but the accident rate is going down. Every day, major US airlines fly over 47,777 flights (not counting smaller planes or cargo planes). In 2014, there were 12 accidents responsible for 641 deaths, out of 3.3 billion passengers. In 1954, there were 87 crashes, with 1600 killed, out of only 141 million passengers.

There are obviously a number of reasons for this improvement, the most prominent one being better engineering and better technology. But part of the improvement is also because the airlines themselves realized that every flight provided valuable information. Unexpected turbulence does not care if you are flying United Airlines or Aer Lingus. By pooling safety data, airlines have a comprehensive view of every factor of every flight, which they can use to make flying more secure.

This sharing of data is a great lesson for the automotive industry and one that the government is trying to encourage. In September 2016, the National Highway Traffic Safety Administration (NHTSA) released their Automated Vehicle Policy, a 116-page document laying out the groundwork for the automotive industry’s future. In it, they recommend creating a shared repository of knowledge regarding accidents, near-misses, and scenarios in which humans have to intervene – basically, every factor that makes self-driving or human-assisted vehicles (HAVs) risky.

In their words: “Such shared data would help to accelerate knowledge and understanding of HAV performance, and could be used to enhance the safety of HAV systems and to establish consumer confidence in HAV technologies.”

Complying with these standards would be incredibly beneficial to OEMs, but the idea has been met with some resistance, for mainly three reasons: 1) there will be an unprecedented amount of data to collect, manage, and store; 2) car companies are not used to this sort of data sharing; and 3) cybersecurity concerns must be addressed first. All of these challenges, however, can be overcome.

Objections and Solutions to Automotive Data Sharing

The first argument that there is too much data has real merit. We talked about how airlines are flying more miles and more passengers these days, but they still cannot compare to cars. There are roughly 20,000 commercial and cargo airplanes in the world and there are over 1.2 billion cars – the staggering difference in data is overwhelming.

OEMs and technology companies are already working on how to best handle the massive volume of data that connected cars are now producing. Innovations like graph databases help make the performance similarities between vehicles clear and accessible. In other words, OEMs are already receiving and analyzing big data. They will just need to adapt to an increased volume of data.

Of course, this raises the question of how the data should be handled. Car companies and their technology partners have been reluctant to share proprietary information with competitors, and for good reason. A shared databank would require an entirely new business model. But this does not mean that auto executives will be forced to sit together in a room and spill company secrets to one another. As the NHTSA report makes sure to emphasize, “Generally, data shared with third parties should be de-identified (i.e., stripped of elements that make the data directly or reasonably linkable to a specific HAV owner or user). Manufacturers and other entities should take steps to ensure that data shared are in accordance with privacy and security agreements and notices applicable to the vehicle (which typically permit sharing of de-identified data) or with owner/user consent.”

So data would be handled in the same manner as the airline model (and indeed, automotive companies and airlines have started exchanging tips on how to handle shared data). However, the comparison still raises one key difference – not only does each OEM’s data constitute proprietary information, each car also contains enormous amounts of personal customer data.

This is why cybersecurity and data privacy are such vital issues. OEMs are not just protecting their own information. They are also protecting their buyers’ information. To continue to do so, cybersecurity techniques must be responsive enough to protect vehicles against hacks from all vulnerable points — including the transfer of data to a third party. Another window, after all, is another place to break in. But cybersecurity backed by over-the-air capabilities can address this problem, allowing OEMs to instantly respond to threats while earning drivers’ trust.

This kind of trust is extremely important when it comes to connected cars. As we have mentioned before, right now, only 32.7% of drivers are willing to put their personal information in the hands of auto manufacturers. However, this is not to say that drivers do not want to do so, eventually. 66.8% say that they would be willing to share information to improve vehicle quality. Luckily, that is exactly what a shared database would be for.

If the promise of connected cars is to be fulfilled, drivers have to trust OEMs with their personal data. They will do so if cars are safe. Making self-driving cars safe requires that same spirit of sharing. Pooling safety data is not about giving up control, it is about letting your cars have the most control possible. Sharing automotive data will help self-driving cars take flight.

As the auto industry is changed by technological and economic currents, OEMs and Tier-1 manufacturers will need to partner with technological specialists to thrive in the era of the software defined car. Movimento’s expertise is rooted in our background as an automotive company. This has allowed us to create the technological platform that underpins the future of the software driven and self-driven car. Connect with us today to learn more about how we can work together.

Imagine a fleet of twenty trucks rolling down some vast, endless highway. Some go faster, some slower, others falter as they pass through differing weather conditions. As they inch their way through hundreds of thousands of miles, the trucks start giving out. Tires are worn out at uneven rates. Finally, there is just one vehicle left, whose tires seem to have outlasted all the others’.

This particular truck incorporated predictive analytics. Each truck drove the way it thought best, but only one used data analysis to understand what that truly meant. As trucking companies try to pass the half-million-mile mark for tires, automakers are struggling to understand how they can extend the lifespan of every piece of hardware on a vehicle. This can be achieved by analyzing every single thing that happens to every single car on the road and then using this data to update hardware, perform preventative maintenance and avoid expensive recalls or repairs. It is not just about avoiding blowouts, it is also about transforming the relationship between the car and the road.

How Can Data Analytics Reduce Recalls?

In 2015, the US auto industry saw a record number of recalled vehicles – 51.2 million – spread across 868 separate recalls. In 2016, the number increased to 53.2 million. This does not mean that cars are somehow getting worse, this has more to do with increased vigilance and better knowledge of faulty systems –similar to finding that there has been a record number of people diagnosed with a disease, rather than it indicating an epidemic, it often means that we are in a better position to detect and cure the illness.

Suppose a car performed really well, with an excellent safety record, except for one thing – when there was a little bit of rain, the braking software read it as a lot of rain and acted accordingly, changing the speed and causing the car to stop more suddenly that it should. Over time, this would impact the brakes, creating a potentially dangerous situation, but this would be virtually impossible to detect ahead of time — unless, of course, the automaker could analyze billions of pieces of data to discover that vehicles in areas with light rainfall were performing differently — a possibility now thanks to big data analytics.

Once the problem is found, getting to a solution does not take long. At this point, there could even be a couple of options. The automaker could arrange a recall to fix the impacted hardware, or they could send targeted Over-The-Air (OTA) software updates to the cars on the road in order to adjust the rain-sensing algorithm. It depends on the situation, of course, but they might be able to avoid a recall altogether.

The Power of Prediction

Predictive analytics goes further than just diagnosing the problem. Perhaps its most important function is the ability to enable OEMs to send out software updates to compensate for specific road conditions. Different drivers have different driving styles and lifestyles, and the hardware that powers their cars will be impacted accordingly. Someone driving in the damp Pacific Northwest mountains has a different experience than someone driving in the stop-and-go flatness of Chicago traffic. Understanding what every car is experiencing can help OEMs design future models and keep current vehicles on the road longer by performing individual adjustments and preventive maintenance.

Other benefits of understanding this information include:

Early identification of hazards: It would be better to diagnose potential problems before they impact anyone at all. By relying on comprehensive data visualization techniques, OEMs may be able to discern that, say, certain cars will have braking issues before the first faulty stop.

Supply chain management: Predictive analytics can provide a bird’s view of which parts might be needed when. Parts will still wear out eventually, of course, but automakers will be able to understand when a spate of breakdowns is likely to occur in China, for instance, due to conditions and time of purchase, well in advance, allowing them enough time to have an optimum supply of the required parts. The global supply chain should be more proactive than reactive. Being able to anticipate the need for updates, especially recalls, can help OEMs manage them in a timely manner.

Improved customer satisfaction: It does not matter if a customer knows that a recall will make them safer. They first think of the inconvenience that it caused them and the fact that they were potentially in danger. Avoiding recalls will increase customer confidence and help protect OEMs’ reputations.

Reputations can also be protected with OTA software updates, which can improve vehicle performance without the drivers needing to come into the dealership with their vehicles. Using adaptive delta compression, software updates can make safe and efficient changes to a car’s performance, further reducing the need for recalls.

Predictive analytics make it possible to prevent bad situations due to faulty hardware. OTA technology makes the transmission of software updates that protect that hardware quick and painless. We will never have a tire that lasts forever but by understanding everything that might happen to that tire, in every condition, rolling down every mile of the highway, we can ensure that the tires and every other piece of hardware that powers a car last longer than before.

As the auto industry is changed by technological and economic currents, OEMs and Tier-1 manufacturers will need to partner with technological specialists to thrive in the era of the software defined car. Movimento’s expertise is rooted in our background as an automotive company. This has allowed us to create the technological platform that underpins the future of the software driven and self-driven car. Connect with us today to learn more about how we can work together.

]]>OTA Technology Can Enable Self-Driving Trucks Through Retrofitted Hardwarehttps://movimentogroup.com/blog/ota-technology-can-enable-self-driving-trucks-retrofitted-hardware/
Tue, 24 Oct 2017 15:41:15 +0000https://movimentogroup.com/?p=5253As we enter into the era of the self-driving truck, OEMs have few precedents to fall back on. When the ...

As we enter into the era of the self-driving truck, OEMs have few precedents to fall back on. When the shipping industry first started using combustion engines instead of horses, there were no hybrids; no one attached a horse to a car with a motor. A vehicle was either horse-driven or it was not. But such clear-cut distinctions are not made in today’s market.

As we progress through the levels of automation, the shift towards self-driving trucks will be gradual. This means that equipment manufacturers must figure out how to prepare for a future that is still rapidly evolving. Fleet owners will not want to keep traditional trucks on the road until self-driving technology is fully fledged, as this will waste time and make them less competitive. At the same time, building trucks right now that have autonomous features that cannot be legally used yet is a waste of resources.

Luckily, these are not the only two options. Trucks, including legacy vehicles, have the ability to be retrofitted with hardware that can be re-programmed with self-driving software as and when it becomes available. That is exactly what Tesla is doing – adding cameras, sensors, and other controls to its latest vehicle models so that new functions can be deployed as soon as the right software is developed. The company will simply send secure OTA software updates to activate these features. Using Over-The-Air (OTA) software updates, commercial vehicles can gradually become more efficient, safer and more autonomous.

Why Retrofitting Commercial Vehicles Makes Financial Sense

In October 2016, Uber announced its first delivery via a self-driving truck. The tractor-trailer, carrying a cargo of beer, drove 120 miles on its own. This event marked a milestone in the shipping industry. Now scrambling to catch up, every truck manufacturer and shipping company is preparing for laws to change and autonomous technology to take off.

While the urgency of adapting to this new shift is understandable, building an entirely new fleet from scratch is not feasible for many companies. Tractor trailers cost up to $175,000 to manufacture, with $125,000 of that spent on the tractor alone. It will also take years for some of the autonomous models currently in development to become widely available. Even Daimler, whose Freightliner is setting the standard for self-driving trucks, does not think the model will really start penetrating the market until 2025. In other words, building an autonomous fleet from the ground up will require a massive amount of time and resources.

This is where retrofitting comes in. Led by Otto, the company that retrofitted trucks for Uber’s historic beer run, a full tractor-trailer can now be retrofitted for $30,000, turning a normal vehicle into a smart, connected, and semi-autonomous vehicle. The successful completion of the European Truck Platooning Challenge by a convoy of autonomous trucks traveling across Europe in 2016 set an example of how this transition can reduce traffic congestion, driver fatigue, number of accidents, and the consumption of fuel. With Otto’s retrofit, a truck can perform several basic functions on its own, including:

Staying in one lane

Slowing down

Accelerating

Maintaining a specified speed

These functions are still limited. The truck cannot pass other vehicles, for instance, as it cannot change lanes on its own. Instead, it might travel with the speed of the car in front of it, maintaining roughly the same velocity. Despite requiring human intervention, this technology is a huge leap forward. The fact that it can be applied to pre-existing vehicles is an incentive to shipping companies. Despite a relatively low upfront cost, it represents a way to save enormous amounts of money by reducing labor costs and lowering delivery times. Self-driving trucks do not have to stop, and will not make wrong turns.

Retrofitting Allows Continuous Software Updates

The need for human intervention and the limitations of the Otto retrofit do not mark the ceiling of retrofitted technology, but the floor. Once the right hardware is installed, each vehicle can be continuously improved.

OEMs and fleet owners/operators can install self-driving hardware onto legacy vehicles for a low cost now. When new technology arrives (possibly within the next decade), the hardware to enable the new software technology will already be in place. With Tesla’s model, trucks can be equipped with cameras, sensors, and also with the ability of each vehicle to communicate with other trucks and with the highway infrastructure itself. It will then be just a matter of using the OTA platform, made secure by industry-best cybersecurity standards, to update new features.

By retrofitting existing models with the right hardware, and through partnerships with companies that can enable the hardware to host new software technology through secure OTA software updates, the commercial trucking industry can be ready for whatever is around the next bend in the road.

As the auto industry is changed by technological and economic currents, OEMs and Tier-1 manufacturers will need to partner with technological specialists to thrive in the era of the software defined car. Movimento’s expertise is rooted in our background as an automotive company. This has allowed us to create the technological platform that underpins the future of the software driven and self-driven car. Connect with us today to learn more about how we can work together.

]]>The Global Connected Car Market: Regional Ownership Modelshttps://movimentogroup.com/blog/global-connected-car-market-regional-ownership-models/
Tue, 17 Oct 2017 09:30:34 +0000https://movimentogroup.com/?p=5223Turning an eye to the United States, travel writer Robert D. Kaplan once noted that people in the gleaming St. ...

Turning an eye to the United States, travel writer Robert D. Kaplan once noted that people in the gleaming St. Louis office towers are far more culturally and economically connected to their counterparts in Berlin or Shanghai than to the people just across the Mississippi River. The world is not flat, but it is connected in strange ways.

This trend is increasingly true in the automotive industry, especially when it comes to connected cars. As markets in developing nations open up, automakers should look at trends based on regional units, not based on countries. Cities and suburban areas in the same country will have different needs. Understanding these shifting connections will help OEMs forge their way into a new territory.

Ride Sharing Services in London

For years, London has been trying to ease its traffic and congestion, going so far as to institute a ‘congestion charge’ for people driving during rush hour. What has finally made a difference are ride-sharing and car-hailing services, as an increasing number of young Londoners opt out of vehicle ownership? It is estimated that there are 25,000 fewer cars in London thanks to these types of services. Before Uber was banned in London, the company had 15,000 drivers on the road within just two years of its launch. The Uber app downloads spiked by 850%. London is not alone. The same trend can be seen in major cities across the United States. Uber estimates that it will take a million cars off the road in New York City alone. The Transportation Research Center has found that:

The percentage of U.S. households without a car is at 9.2%, up from 8.7% in 2007.

The percentage of households without a car has increased in 21 of the 30 largest U.S. cities over the past nine years.

In six of those 30 cities, at least 30% of households are without a car; 56.5% of New York’s population does not own one.

In short, major cities are progressively reducing the number of cars on the road, a trend that seems poised to spread across the globe. As places like Shanghai, Mumbai, and Lagos begin to grapple with the future of urban development, they will look to ride-sharing services like Uber and Lyft for convenient transport options.

Understanding Future Markets in a Multi-Level World

When GM announced a partnership with Lyft in 2016, it was clear that they were already planning for a future where there will be fewer cars in urban areas and the cars that do exist will be different. There is a good chance that they will belong to an autonomous fleet, owned by businesses that run them on the Uber/Lyft model, or managed by governments, or even operated by the OEMs themselves.

This model is now being discussed for regions as diverse as economic powerhouses like China and developing nations like Nigeria or Uganda. The former is looking to decrease traffic and pollution; the latter are dealing with car ownership circumscribed by economic opportunity. Self-driving cars and connected technology, which can transform cities in the United States and Europe, can do the same thing around the globe as well. There is not one ‘China market’ for cars. There are lots of China markets, and demand in Shanghai or Beijing or Guangzhou will easily match that of Chicago or London more than the sub-urban provinces of the same country. This is why it is important for OEMs to understand how different markets are connected. More than 50% of the human population now lives in urban areas — this number still excludes billions of people. Even Nigeria, with some of the other fastest-growing cities on the planet, has an urban population under 50%.

That means that there are still many markets that need to be catered to in ways that are both increasingly atomized and uniquely global. Being able to provide cars that are designed for each environment, whether it is the city streets of London or the suburbs of Iowa, will require technology that keeps services flexible and adaptive. The technological flexibility that can target the right cars in the right areas and provide updates without the need for a recall will contribute to the efficiency of this model.

The world is undergoing an enormous transformation – politically, economically and technologically. Cities and the surrounding suburban areas are now more connected to counterparts across the globe than to each other. OEMs should be able to adapt to this changing reality and with the right technology, they can lead the way.

As the auto industry is changed by technological and economic currents, OEMs and Tier-1 manufacturers will need to partner with technological specialists to thrive in the era of the software defined car. Movimento’s expertise is rooted in our background as an automotive company. This has allowed us to create the technological platform that underpins the future of the software driven and self-driven car. Connect with us today to learn more about how we can work together.

Movimento CEO and President, Ben Hoffman, was featured on IBM’s Genius of Things Summit in Boston on October 4th, 2017 where he emphasized on the transformation of the automotive industry towards the Software Defined Car™ and Smart Mobility Services. During the session he asked a very important question – How do you engage with an autonomous vehicle? The pace of change is accelerating globally in all industries but there are certain megatrends that are driving the automotive industry including:

Safety: The idea that there would be zero deaths from accidents in the future is now possible.

Green: Electrification of vehicles and reduction in emissions are becoming very popular in the automotive industry.

Connected: Connectivity is the foundation of automated driving and mobility services.

End Markets: Secure connectivity enables traditional OEMs to become new service providers.

Ben concluded by saying that the complexity, mobility and the global reach of the automotive industry has the potential to lead other industries in terms of software and connectivity.